# ---encoding:utf-8---
# @Time    : 2024/9/9 12:28
# @Author  : DuJingze
# @Email   ：
# @Site    : 
# @File    : scan.py
# @Project : 12_GuPaoPytorch
# @Software: PyCharm
# 导入工具包
import numpy as np
import argparse
import cv2

# 设置参数
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True, help="Path to the image to be scanned")
args = vars(ap.parse_args())

def zh_ch(string):
    return string.encode('gbk').decode(errors='ignore')

def order_points(pts):
    # 一共4个坐标点
    rect = np.zeros((4, 2), dtype="float32")

    # 按照顺序找到对应坐标0123，分别对应 左上 右上 右下 左下
    # 计算左上、右下
    s = pts.sum(axis=1)
    rect[0] = pts[np.argmin(s)]
    rect[2] = pts[np.argmax(s)]
    # 计算右上、左下
    diff = np.diff(pts, axis=1)
    rect[1] = pts[np.argmin(diff)]
    rect[3] = pts[np.argmax(diff)]
    return rect


def four_point_transform(image, pts):
    # 获取输入坐标点
    rect = order_points(pts)
    tl, tr, br, bl = rect

    # 计算输入的w和h值
    widthA = np.sqrt(((br[0] - bl[0] ** 2) + (br[1] - bl[1] ** 2)))
    widthB = np.sqrt(((tr[0] - tl[0] ** 2) + (tr[1] - tl[1] ** 2)))
    maxWidth = max(int(widthA), int(widthB))

    heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))
    heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))
    maxHeight = max(int(heightA), int(heightB))

    # 变换后对应坐标位置
    dst = np.array([
        [0, 0],
        [maxWidth - 1, 0],
        [maxWidth - 1, maxHeight - 1],
        [0, maxHeight - 1]], dtype="float32"
    )

    # 计算变换矩阵
    M = cv2.getPerspectiveTransform(rect, dst)
    warped = cv2.warpPerspective(image, M, (maxWidth, maxWidth))

    # 返回变换后结果
    return warped


def resize(image, width=None, height=None, inter=cv2.INTER_AREA):
    dim = None
    h, w = image.shape[:2]
    if width is None and height is None:
        return image
    if width is None:
        r = height / float(h)
        dim = (int(w * r), height)
    else:
        r = width / float(w)
        dim = (width, int(h * r))
    resized = cv2.resize(image, dim, interpolation=inter)
    return resized


if __name__ == '__main__':
    # 读取输入
    image = cv2.imread(args["image"])
    # 坐标也会相同变化
    ratio = image.shape[0] / 500.0
    orig = image.copy()
    image = resize(orig, height=500)

    # 预处理
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    gray = cv2.GaussianBlur(gray, (5, 5), 0)
    edge = cv2.Canny(gray, 75, 200)

    # 展示预处理结果
    print("STEP 1: 边缘检测")
    cv2.imshow(zh_ch("Image原图"), image)
    cv2.imshow(zh_ch("Edge边缘检测"), edge)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

    # 轮廓检测
    cnts = cv2.findContours(edge.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)[1]
    cnts = sorted(cnts, key=cv2.contourArea, reverse=True)[:5]

    # 遍历轮廓
    for c in cnts:
        # 计算轮廓近似
        peri = cv2.arcLength(c, True)
        # C 表示输入的点集
        # epsilon 表示从原始轮廓到近似轮廓的最大距离，它是一个准确度参数
        # True 表示是封闭的
        approx = cv2.approxPolyDP(c, 0.02 * peri, True)
        # 4 个点的时候就拿出来
        if len(approx) == 4:
            screenCnt = approx
            break
    # 展示结果
    print("STEP 2: 获取轮廓")
    cv2.drawContours(image, [screenCnt], -1, (0, 255, 0), 2)
    cv2.imshow(zh_ch("Outline 获取轮廓"), image)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

    # 透视变换
    warped = four_point_transform(orig, screenCnt.reshape(4, 2) * ratio)

    # 二值处理
    warped = cv2.cvtColor(warped, cv2.COLOR_BGR2GRAY)
    ref = cv2.threshold(warped, 100, 255, cv2.THRESH_BINARY)[1]
    cv2.imwrite('scan.jpg', ref)
    # 展示结果
    print("STEP 3: 变换")
    cv2.imshow("Original", resize(orig, height=650))
    cv2.imshow("Scanned", resize(ref, height=650))
    cv2.waitKey(0)
